We compare the network complexity of CsiNet [1]}, CRNet-cosine [2]} and our proposed ENet with respect to the number of network parameters in Table I. The parameters of the convolutional layer, the deconvolutional layer, and the fully-connected layer account for the majority of the complexity of the NN, and thus we focus on these components.
From Table I, ENet is a parameter-saving NN compared to both CsiNet and CRNet-cosine. For \(f=16\) , ENet reduces the number of network parameters by over 80% at all compression ratios. For \(f=32\) , 60% parameters are saved. Fewer parameters not only mean memory-saving, but also require fewer data samples for training and can alleviate overfitting.
